Developing an Automated Technique to Calibrate the AASHTOWare Pavement ME Design Software

2020 ◽  
Vol 2674 (9) ◽  
pp. 867-877
Author(s):  
Shuvo Islam ◽  
Avishek Bose ◽  
Christopher A. Jones ◽  
Mustaque Hossain ◽  
Cristopher I. Vahl
Keyword(s):  
Goodness Of Fit ◽  
Optimization Technique ◽  
Model Verification ◽  
Statistical Testing ◽  
P Value ◽  
State Highway ◽  
Global Calibration ◽  
Automated Technique ◽  
State Highway Agencies ◽  
Pavement Me Design

Many state highway agencies are in the process of implementing the AASHTOWare Pavement ME Design (PMED) software for routine pavement design. However, a recurring implementation challenge has been the need to locally calibrate the software to reflect an agency’s design and construction practices, materials, and climate. This study introduced a framework to automate the calibration processes of the PMED performance models. This automated technique can search PMED output files and identify relevant damages/distresses for a project on a particular date. After obtaining this damage/distress information, the technique conducts model verification with the global calibration factors. Transfer function coefficients are then automatically derived following an optimization technique and numerical measures of goodness-of-fit. An equivalence statistical testing approach is conducted to ensure predicted performance results are in agreement with the measured data. The automated technique allows users to select one of three sampling approaches: split sampling, jackknifing, or bootstrapping. Based on the sampling approach chosen, the automated technique provides the calibration coefficients or suitable ranges for the coefficients and shows the results graphically. Model bias, standard error, sum squared error, and p-value from the paired t-test are also reported to assess efficacy of the calibration process.

Hubungan Religiusitas dengan Citra Tubuh pada Wanita Dewasa Awal

2018 ◽  
Vol 1 (1) ◽  
pp. 9-28
Author(s):  
Dessy Sumanty ◽  
Deden Sudirman ◽  
Diah Puspasari
Keyword(s):  
Body Image ◽  
Correlation Coefficient ◽  
Research Design ◽  
Calculation Result ◽  
The Body ◽  
Statistical Testing ◽  
P Value ◽  
Significance Level ◽  
Correlational Research

This research attempts to relate the body image phenomenon with the level of subject religiosity. This research used correlational research design that was involving 332 respondents. The statistical testing which is used to test the hypothesis Rank Spearman. The calculation result with the significance level of trust 95% (a = 0.05) show that the correlation coefficient is 0.083 and p-value is 0.129. It means that Ho is accepted and H1 is rejected. It can be concluded that there is no relationship between religiosity with body image.

scholarly journals Fitting Terrestrial Laser Scanner Point Clouds with T-Splines: Local Refinement Strategy for Rigid Body Motion

2021 ◽  
Vol 13 (13) ◽  
pp. 2494
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Point Cloud ◽  
Goodness Of Fit ◽  
Laser Scanner ◽  
Point Clouds ◽  
Statistical Testing ◽  
Computational Time ◽  
Terrestrial Laser Scanner ◽  
Reference Surface ◽  
Local Refinement ◽  
Surface Approximation

T-splines have recently been introduced to represent objects of arbitrary shapes using a smaller number of control points than the conventional non-uniform rational B-splines (NURBS) or B-spline representatizons in computer-aided design, computer graphics and reverse engineering. They are flexible in representing complex surface shapes and economic in terms of parameters as they enable local refinement. This property is a great advantage when dense, scattered and noisy point clouds are approximated using least squares fitting, such as those from a terrestrial laser scanner (TLS). Unfortunately, when it comes to assessing the goodness of fit of the surface approximation with a real dataset, only a noisy point cloud can be approximated: (i) a low root mean squared error (RMSE) can be linked with an overfitting, i.e., a fitting of the noise, and should be correspondingly avoided, and (ii) a high RMSE is synonymous with a lack of details. To address the challenge of judging the approximation, the reference surface should be entirely known: this can be solved by printing a mathematically defined T-splines reference surface in three dimensions (3D) and modeling the artefacts induced by the 3D printing. Once scanned under different configurations, it is possible to assess the goodness of fit of the approximation for a noisy and potentially gappy point cloud and compare it with the traditional but less flexible NURBS. The advantages of T-splines local refinement open the door for further applications within a geodetic context such as rigorous statistical testing of deformation. Two different scans from a slightly deformed object were approximated; we found that more than 40% of the computational time could be saved without affecting the goodness of fit of the surface approximation by using the same mesh for the two epochs.

scholarly journals Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

2021 ◽  
Vol 503 (2) ◽  
pp. 2688-2705
Author(s):  
C Doux ◽  
E Baxter ◽  
P Lemos ◽  
C Chang ◽  
A Alarcon ◽  
...  
Keyword(s):  
Dark Energy ◽  
Internal Consistency ◽  
Goodness Of Fit ◽  
Small Scale ◽  
P Value ◽  
Posterior Predictive Distribution ◽  
Data Set ◽  
Dark Energy Survey ◽  
Data Vector ◽  
Energy Survey

ABSTRACT Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. We present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties

2021 ◽  
pp. 036119812110237
Author(s):  
Laura Camarena
Keyword(s):  
Machine Learning ◽  
Material Properties ◽  
Resilient Modulus ◽  
Nonlinear Behavior ◽  
Machine Learning Techniques ◽  
Design Parameters ◽  
Nonlinear Parameters ◽  
State Highway ◽  
Learning Techniques ◽  
State Highway Agencies

The Mechanistic–Empirical Pavement Design Guide (MEPDG) considers a hierarchical approach to determine the input values necessary for most design parameters. Level 1 requires site-specific measurement of the material properties from laboratory testing, whereas other levels make use of equations developed from regression models to estimate the material properties. Resilient modulus is a mechanical property that characterizes the unbound and subgrade materials under loading that is essential for the mechanistic design of pavements. The MEPDG resilient modulus model makes use of a three-parameter constitutive model to characterize the nonlinear behavior of the geomaterials. As the resilient modulus tests are complex, expensive, and require lengthy preparation time, most state highway agencies are unlikely to implement them as routine daily applications. Therefore, it is imperative to make use of models to calculate these nonlinear parameters. Existing models to determine these parameters are frequently based on linear regression. With the development of machine learning techniques, it is feasible to develop simpler equations that can be used to estimate the nonlinear parameters more accurately. This study makes use of the Long-Term Pavement Performance database and machine learning techniques to improve the equations utilized to determine the nonlinear parameters crucial to estimate the resilient modulus of unbound base and subgrade materials.

Determining Minimum Contract Time for Highway Projects

2000 ◽  
Vol 1712 (1) ◽  
pp. 196-201 ◽  
Author(s):  
Jin-Fang Shr ◽  
Benjamin P. Thompson ◽  
Jeffrey S. Russell ◽  
Bin Ran ◽  
H. Ping Tserng
Keyword(s):  
Low Cost ◽  
Road User ◽  
User Cost ◽  
Time Curve ◽  
Highway Projects ◽  
The Road ◽  
State Highway ◽  
Florida Department ◽  
The Cost ◽  
State Highway Agencies

An increasing number of state highway agencies (SHAs) are using A (cost) + B (time cost) bidding ( A + B bidding) for highway construction. The A + B bidding concept is designed to shorten the total contract time by allowing each contractor to bid the number of days in which the work can be accomplished, in addition to the traditional cost bid. The SHA is then presented with the problem of determining a reasonable range of contract time submitted by the bidders. Most SHAs do not currently restrict the range of B. However, several problems may arise from an unrestricted range of B. First, if no minimum is set for B, a bidder may inflate the cost bid and submit an unreasonably low B, using the excess cost bid to cover the disincentives charged for exceeding the time bid. Second, if no maximum is set for B, then a bidder with a high B and a low-cost bid may be awarded the job and make an unreasonable amount of money from incentive payments. This study develops a quantified model of the price-time bidding contract. A construction cost-versus-time curve is developed from Florida Department of Transportation (DOT) data. The contractor’s price-versus-time curve is then combined with the road-user cost to determine the optimum lower limit to be set on B. Finally, several projects completed by the Florida DOT will be used to illustrate this model.

Pay Factor Analysis for Highway Pavements through Percentage of Material within Specification Limits and Monte Carlo Simulation

2021 ◽  
pp. 036119812110255
Author(s):  
Yunpeng Zhao ◽  
Dimitrios Goulias
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Operating Characteristic ◽  
Production Quality ◽  
Specification Limits ◽  
State Highway ◽  
Long Run ◽  
Similar Materials ◽  
Pay Factor ◽  
State Highway Agencies

Many state highway agencies (SHAs) have adopted pay adjustment provisions in their acceptance plans for construction and materials. In these payment adjustment acceptance plans, the percentage of material within specification limits (PWL) has been selected as the quality measure to relate production quality to pay factors, and pay equations are used to determine a pay factor for a lot based on PWL. Various pay equations have been proposed by the highway community for adoption in SHAs’ specifications. However, the effectiveness of these pay equations has not been fully evaluated. Another issue concerning the pay adjustment acceptance plans is the risk associated with single and multiple pay factors. The purpose of this study was to evaluate the effects of different pay equations commonly used by SHAs and the risks associated with pay adjustment acceptance plans. This was achieved by developing operating characteristic curves associated with various pay factors and expected pay curves and Monte Carlo simulation for assessing the effects in the long run. The methodology suggested in this paper is transferable elsewhere where similar materials and specifications are used for the acceptance of pavements.

DISTRIBUTIONAL ANALYSIS OF RELATED SYNSETS IN WordNet FOR A WORD SENSE DISAMBIGUATION TASK

2005 ◽  
Vol 14 (06) ◽  
pp. 919-934 ◽  
Author(s):  
KOSTAS FRAGOS ◽  
YANIS MAISTROS
Keyword(s):  
Target Word ◽  
Goodness Of Fit ◽  
Hypothesis Test ◽  
Word Sense Disambiguation ◽  
The Other ◽  
Reasonable Assumption ◽  
P Value ◽  
Word Sense ◽  
Chi Square ◽  
Sense Disambiguation

This work presents a new method for an unsupervised word sense disambiguation task using WordNet semantic relations. In this method we expand the context of a word being disambiguated with related synsets from the available WordNet relations and study within this set the distribution of the related synset that correspond to each sense of the target word. A single sample Pearson-Chi-Square goodness-of-fit hypothesis test is used to determine whether the null hypothesis of a composite normality PDF is a reasonable assumption for a set of related synsets corresponding to a sense. The calculated p-value from this test is a critical value for deciding the correct sense. The target word is assigned the sense, the related synsets of which are distributed more "abnormally" relative to the other sets of the other senses. Our algorithm is evaluated on English lexical sample data from the Senseval-2 word sense disambiguation competition. Three WordNet relations, antonymy, hyponymy and hypernymy give a distributional set of related synsets for the context that was proved quite a good word sense discriminator, achieving comparable results with the system obtained the better results among the other competing participants.

scholarly journals The Development of Indicators for Transformational Leadership of Undergraduate Students at Thailand National Sports University

2021 ◽  
Vol 11 (1) ◽  
pp. 94
Author(s):  
Thitipong Sukdee
Keyword(s):  
Transformational Leadership ◽  
Undergraduate Students ◽  
Goodness Of Fit ◽  
Rating Scale ◽  
Structured Interview ◽  
P Value ◽  
Sample Population ◽  
Research Instrument ◽  
Fit Index

This paper develops indicators for transformational leadership of undergraduate students at Thailand National Sports University. The development of these indicators was based on in-depth interviews with six informants. The research instrument was a semi-structured interview form. The data collected were inductively analyzed. The development of indicators was accomplished through the synthesis of the data and the conceptual framework for this research investigation. Evaluate indicators of Accuracy Standard Propriety Standard with of five experts was compared with the principle of 3.50, using the research instrument was an Evaluation Form and thence the verification of the quality of the congruence of the indicators. The sample population consisted of 4 5 0 undergraduate students at Thailand National Sports. The research instrument was a rating-scale questionnaire. Confirmatory factor analysis (CFA) was utilized in conjunction with reliance on the maximum likelihood estimation (MLE) technique. Findings were as follows: 1) In regard to the development of indicators for transformational leadership of undergraduate students at Thailand National Sports University, results were as follows: The aspect of idealized influence engendered eight keywords and twenty-two subtopics. The aspect of inspiration motivation exhibited six keywords and thirteen subtopics. The aspect of intellectual motivation evinced six keywords and fourteen subtopics. The aspect of individualized consideration displayed five keywords and eleven subtopics. 2) Assessing indicators for transformational leadership of undergraduate students at Thailand National Sports University All aspects were significantly higher than the threshold 3.50 at the .05 level. 3) The development of indicators for transformational leadership of undergraduate students at Thailand National Sports University were found to have twenty-five indicators. The verification of the quality of appropriateness for the indicators for transformational leadership of undergraduate students at Thailand National Sports University showed that chi-square (χ2) was at 552.93; (probability-value [p-value] at 0.000); degrees of freedom (df) at 271; relative χ2 at 2.04; goodness of fit index (GFI) at 0.91; adjusted goodness of fit index (AGFI) at 0.90; Normal Fit Index (NFI) at 0.99; Comparative Fit Index (CFI) at 0.99 and the root mean square error of approximation (RMSEA) at 0.04. It is concluded that transformational leadership of undergraduate students at Thailand National Sports is consistent with empirical data.

Integrating Visualization into Structured Public Involvement: Case Study of Highway Improvement in Central Kentucky

2002 ◽  
Vol 1817 (1) ◽  
pp. 50-57 ◽  
Author(s):  
Keiron Bailey ◽  
Joel Brumm ◽  
Ted Grossardt
Keyword(s):  
User Satisfaction ◽  
Public Involvement ◽  
Systems Approach ◽  
Three Dimensional ◽  
Factors Affecting ◽  
State Highway ◽  
Advanced Technologies ◽  
Design Options ◽  
State Highway Agencies

Public involvement in transportation infrastructure decision making is frequently mandated and is regarded as increasingly essential by a wide variety of stakeholders. The integration of advanced technologies, such as visualization, into this process is increasingly desired. However, public involvement processes often are regarded as problematic by many stakeholders and the state highway agencies charged with implementing them. Structured public involvement (SPI) is posited. SPI takes a systems approach toward the integration of advanced technologies into public involvement forums. Because the goal of public involvement is to increase user satisfaction with both the process and the outcomes, the characteristics of advanced technologies and their capacities for gathering useful feedback in public forums must be evaluated. Visualization is put forth as an enabling technology within an SPI framework. The properties, capacities, and transportation-related uses of three visualization modes are evaluated, and their operational features are discussed. A case study dealing with highway improvement in central Kentucky reveals that three-dimensional renderings are significantly preferred to twodimensional and virtual reality modes; the case study also shows that visualization should complement, not replace, other performance information. The role of electronic scoring as an integral component of this SPI protocol is emphasized, resulting in fast assessment and free expression of views. Factors affecting the efficiency of visualization are analyzed, and recommendations are presented for implementing SPI protocols that rely on visualization. These include investigating participants' previous experience with visualization, incorporating iterative public involvement in finalizing design options, and ensuring that the technologies are compatible with the chosen public involvement process.

The Influence of Brand Extension Smartphone Samsung Galaxy Variable towards Brand Equity of Mother Brand of Samsung

2018 ◽  
Vol 3 (1) ◽  
pp. 06-15
Author(s):  
Sudaryanto Sudaryanto ◽  
Jery Courvisanos ◽  
Alif Puji Rahayu
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Brand Equity ◽  
Perceived Risk ◽  
Goodness Of Fit ◽  
Brand Extension ◽  
Jel Classification ◽  
Brand Extensions ◽  
Statistical Testing ◽  
Validity And Reliability

Objective - The purpose of this study is to identify the influence of similarity, reputation, perceived risk, and innovation as brand extensions of smartphones developed by Samsung, toward brand equity. Methodology/Technique - This study uses explanatory research. The population in this study consists of consumers of Samsung Galaxy mobiles for at least one month. Questionnaires were delivered to the respondents, after it had passed the validity and reliability tests. Following on from the the statistical testing, the data was analysed using a multiple linear regression. Then, the classical assumption test was conducted to determine the goodness of fit of the model. The data was collected using a questionnaire consisting of a closed statement, measured by a Likert Scale Findings - The results of this study show that similarity, reputation, perceived risk, and innovation as the variable dimensions have a significant effect on Brand Equity of Samsung Galaxy mobiles. Type of Paper: Empirical Keywords: Brand Extension; Brand Equity; Similarity; Reputation; Perceived Risk; Innovation; Explanatory Research. JEL Classification: M3, M30, M39.

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